![]() Dixon and Coles were the first to evaluate the strength of football teams for the purpose of generating profit against published market odds with the use of a time-dependent They showed that even though there was no evidence of abnormal returns, there was some indication that the expert opinions were more valuable towards the end of the football season. Pope and Peel evaluated a simulation of bets against published market odds in accordance with the recommendations of a panel of newspapers experts. While numerous academic papers exist which focus on football match forecasts, only a few of them appear to consider profitability as an assessment tool for determining a model's forecasting capability. This is one of the primary reasons why we currently observe extensive attention paid to football odds by both academic research groups and industrial organisations who look to profit from potential market inefficiencies. All rights reserved.Īssociation Football (hereafter referred to as simply football) is the most popular sport internationally, and attracts an increasing share of the multi-billion dollar gambling industry particularly after its introduction online. Compared to a previously published successful BN model, the model presented in this paper is less complex and is able to generate even more profitable returns. Profitability, risk and uncertainty are evaluated by considering various unit-based betting procedures against published market odds. The model was used to generate forecasts for each match of the 2011/ 2012 English Premier League (EPL) season, and forecasts were published online prior to the start of each match. Both objective and subjective information are considered for prediction, and we demonstrate how probabilities transform at each level of model component, whereby predictive distributions follow hierarchical levels of Bayesian inference. We present a Bayesian network (BN) model for forecasting Association Football match outcomes. Keywords: Bayesian networks Expert systems Football betting Football forecasts Subjective information Risk & Information Management (RIM) Research Group, Department of Electronic Engineering and Computer Science, Queen Mary, University of London, London El 4NS, United Kingdom Prediction, risk and uncertainty using Bayesian networks ^Īnthony Costa Constantinou *, Norman Elliott Fenton, Martin Neil Journal homepage: Profiting from an inefficient association football gambling market: ® q Contents lists available at SciVerse ScienceDirect
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